Self-adjusting Genetic Algorithm with Greedy Agglomerative Crossover for Continuous p-Median Problems

Kazakovtsev, Lev; Rozhnov, Ivan; Nasyrov, I R; Orlov, V. I.

doi:10.1007/978-3-030-86433-0_13

Cited by 2 publications

(3 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the problems of evolutionary algorithms is maintaining the diversity of the population [46,56,57]. Eremeev in [58] described the mutation genetic operator as the essential procedure that guarantees population diversity.…”

Section: Diversity Maintenancementioning

confidence: 99%

Formation of Fuzzy Patterns in Logical Analysis of Data Using a Multi-Criteria Genetic Algorithm

Masich

Kulachenko²,

Stanimirović

et al. 2022

Symmetry

View full text Add to dashboard Cite

The formation of patterns is one of the main stages in logical data analysis. Fuzzy approaches to pattern generation in logical analysis of data allow the pattern to cover not only objects of the target class, but also a certain proportion of objects of the opposite class. In this case, pattern search is an optimization problem with the maximum coverage of the target class as an objective function, and some allowed coverage of the opposite class as a constraint. We propose a more flexible and symmetric optimization model which does not impose a strict restriction on the pattern coverage of the opposite class observations. Instead, our model converts such a restriction (purity restriction) into an additional criterion. Both, coverage of the target class and the opposite class are two objective functions of the optimization problem. The search for a balance of these criteria is the essence of the proposed optimization method. We propose a modified evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to solve this problem. The new algorithm uses pattern formation as an approximation of the Pareto set and considers the solution’s representation in logical analysis of data and the informativeness of patterns. We have tested our approach on two applied medical problems of classification under conditions of sample asymmetry: one class significantly dominated the other. The classification results were comparable and, in some cases, better than the results of commonly used machine learning algorithms in terms of accuracy, without losing the interpretability.

show abstract

Section: Diversity Maintenancementioning

confidence: 99%

Formation of Fuzzy Patterns in Logical Analysis of Data Using a Multi-Criteria Genetic Algorithm

Masich

Kulachenko²,

Stanimirović

et al. 2022

Symmetry

View full text Add to dashboard Cite

show abstract

“…inefficient use of network resources [19][20][21]; -lack of prompt response to overloads when generating traffic and changing the network topology in existing satellite routing protocols [1,[22][23][24][25][26][27][28][29][30]; -inability to work with different types of traffic [19,31]; -focusing on a single criteria when solving optimization problems with multiple objectives [19,[21][22][23][24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

“…Many recent papers are devoted to local routing, i.e., the route is determined for a single flow, without considering its influence on others. Hybrid algorithms that combine heuristic approaches with local search are also widely used for location problems [24][25][26], including location on networks, and the application of similar algorithmic combinations for traffic routing seems to be promising.…”

Section: Introductionmentioning

confidence: 99%

Heuristic Greedy-Gradient Route Search Method for Finding an Optimal Traffic Distribution in Telecommunication Networks

Gaipov,

Tausnev,

Khodenkov

et al. 2023

Algorithms

View full text Add to dashboard Cite

Rapid growth in the volume of transmitted information has lead to the emergence of new wireless networking technologies with variable heterogeneous topologies. With limited radio frequency resources, optimal routing problems arise, both at the network design stage and during its operation. We propose an algorithm based on a minimum loss intensity (greedy-gradient algorithm) to search for optimal routes of information transmission in telecommunication networks. The relevance of the developed algorithm is determined by its practical use in data-transmitting modeling systems. The proposed algorithm satisfies several requirements, such as the speed of the calculations performed, the fulfillment of the conditions for its convergence, and its independence on the selected loss probability function, as well as on the network topology. The idea of the algorithm is a step-by-step recalculation of metrics based on derivatives of the loss intensity function with simultaneous redistribution of information flows along the routes determined by the Floyd algorithm. The comparative efficiency of the proposed algorithm is demonstrated by computational experiments on various network topologies (up to 100 nodes) with various traffic intensities.

show abstract

Self-adjusting Genetic Algorithm with Greedy Agglomerative Crossover for Continuous p-Median Problems

Cited by 2 publications

References 41 publications

Formation of Fuzzy Patterns in Logical Analysis of Data Using a Multi-Criteria Genetic Algorithm

Formation of Fuzzy Patterns in Logical Analysis of Data Using a Multi-Criteria Genetic Algorithm

Heuristic Greedy-Gradient Route Search Method for Finding an Optimal Traffic Distribution in Telecommunication Networks

Contact Info

Product

Resources

About